{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([[1.2, 1.5, 1.8],\n",
    "[1.3, 1.4, 1.9],\n",
    "[1.1, 1.6, 1.7]])\n",
    "y = np.array([5, 10, 9]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[37.2, 37.599999999999994, 36.8]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans1_1 = [sum(x * y) for x in X] \n",
    "ans1_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([37.2, 37.6, 36.8])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ans1_2 = np.dot(X,y)\n",
    "ans1_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.61 µs ± 79.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit [sum(x * y) for x in X] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "997 ns ± 3.99 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.dot(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9, 6],\n",
       "       [1, 1, 2],\n",
       "       [8, 7, 3],\n",
       "       [5, 6, 3],\n",
       "       [5, 3, 5],\n",
       "       [8, 8, 2],\n",
       "       [8, 1, 7],\n",
       "       [8, 7, 2],\n",
       "       [1, 2, 9],\n",
       "       [9, 4, 9]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "X = np.random.randint(1, 10, size=30)\n",
    "X= X.reshape(-1, 3)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9, 1],\n",
       "       [1, 1, 0],\n",
       "       [8, 7, 0],\n",
       "       [5, 6, 0],\n",
       "       [5, 3, 1],\n",
       "       [8, 8, 0],\n",
       "       [8, 1, 2],\n",
       "       [8, 7, 0],\n",
       "       [1, 2, 2],\n",
       "       [9, 4, 2]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for x in X:\n",
    "    if x[2] <=3:\n",
    "        x[2] = 0\n",
    "    elif x[2] > 3 and x[2] <=6:\n",
    "        x[2] = 1\n",
    "    elif x[2] > 6:\n",
    "        x[2] = 2\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9],\n",
       "       [1, 1],\n",
       "       [8, 7],\n",
       "       [5, 6],\n",
       "       [5, 3],\n",
       "       [8, 8],\n",
       "       [8, 1],\n",
       "       [8, 7],\n",
       "       [1, 2],\n",
       "       [9, 4]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = X[:,:2]\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 2, 0, 2, 2])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = X[:, 2]\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [8, 7],\n",
       "       [5, 6],\n",
       "       [8, 8],\n",
       "       [8, 7]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[y_train == 0]\n",
    "# group is 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9],\n",
       "       [5, 3]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[y_train == 1]\n",
    "# group is 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8, 1],\n",
       "       [1, 2],\n",
       "       [9, 4]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[y_train == 2]\n",
    "# group is 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2      162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3      162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4      162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    './log.txt', sep = '\\t',\n",
    "    names=['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      "id              179496 non-null int64\n",
      "api             179496 non-null object\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2      162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3      162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4      162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      "id              179496 non-null int64\n",
      "api             179496 non-null object\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 13.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['interval'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['/front-api/bill/create'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0      8       1057.31         88.75        177.72         132.0   \n",
       "1      5        749.12        103.79        240.38         149.0   \n",
       "2      5        845.84        136.31        225.73         169.0   \n",
       "3      9       1305.52         90.12        196.61         145.0   \n",
       "4      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "            created_at  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['interval','api','id'], axis= 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-01 00:00:07      8       1057.31         88.75        177.72   \n",
       "2018-11-01 00:01:07      5        749.12        103.79        240.38   \n",
       "2018-11-01 00:02:07      5        845.84        136.31        225.73   \n",
       "2018-11-01 00:03:07      9       1305.52         90.12        196.61   \n",
       "2018-11-01 00:04:07      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg  \n",
       "created_at                         \n",
       "2018-11-01 00:00:07         132.0  \n",
       "2018-11-01 00:01:07         149.0  \n",
       "2018-11-01 00:02:07         169.0  \n",
       "2018-11-01 00:03:07         145.0  \n",
       "2018-11-01 00:04:07         189.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop('created_at', axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:55:28</th>\n",
       "      <td>7</td>\n",
       "      <td>868.67</td>\n",
       "      <td>84.80</td>\n",
       "      <td>193.69</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:56:28</th>\n",
       "      <td>5</td>\n",
       "      <td>632.94</td>\n",
       "      <td>80.21</td>\n",
       "      <td>198.60</td>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:57:28</th>\n",
       "      <td>2</td>\n",
       "      <td>241.44</td>\n",
       "      <td>56.97</td>\n",
       "      <td>184.47</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:58:28</th>\n",
       "      <td>8</td>\n",
       "      <td>1220.53</td>\n",
       "      <td>127.26</td>\n",
       "      <td>189.14</td>\n",
       "      <td>152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:59:28</th>\n",
       "      <td>4</td>\n",
       "      <td>687.85</td>\n",
       "      <td>93.45</td>\n",
       "      <td>284.42</td>\n",
       "      <td>171.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2018-11-11 23:55:28      7        868.67         84.80        193.69   \n",
       "2018-11-11 23:56:28      5        632.94         80.21        198.60   \n",
       "2018-11-11 23:57:28      2        241.44         56.97        184.47   \n",
       "2018-11-11 23:58:28      8       1220.53        127.26        189.14   \n",
       "2018-11-11 23:59:28      4        687.85         93.45        284.42   \n",
       "\n",
       "                     res_time_avg  \n",
       "created_at                         \n",
       "2018-11-11 23:55:28         124.0  \n",
       "2018-11-11 23:56:28         126.0  \n",
       "2018-11-11 23:57:28         120.0  \n",
       "2018-11-11 23:58:28         152.0  \n",
       "2018-11-11 23:59:28         171.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_in_one_day = df['2018-11-11 00:00:00':'2018-11-12 00:00:00']\n",
    "df_in_one_day.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-11 00:00:00</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 00:05:00</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 00:10:00</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 00:15:00</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 00:20:00</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:35:00</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:40:00</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:45:00</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:50:00</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:55:00</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count\n",
       "created_at                \n",
       "2018-11-11 00:00:00     25\n",
       "2018-11-11 00:05:00     21\n",
       "2018-11-11 00:10:00     16\n",
       "2018-11-11 00:15:00     11\n",
       "2018-11-11 00:20:00     16\n",
       "...                    ...\n",
       "2018-11-11 23:35:00     40\n",
       "2018-11-11 23:40:00     36\n",
       "2018-11-11 23:45:00     34\n",
       "2018-11-11 23:50:00     26\n",
       "2018-11-11 23:55:00     26\n",
       "\n",
       "[288 rows x 1 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_in_one_day =  df_in_one_day.resample('5T').sum()[['count']]\n",
    "count_in_one_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FDE1DJKD7ViraEbjLQgkY9mKUAmmEjeCBK6EO6jlJm0pmkcoWjsCLoSLwruYglrWFcWhY3vGsaIsAkAIesi8IU3akH7CzUNJmLo3QEvPT3YgFnGHqHOVLt4QM9I0mnCgPyBcJdyW9jGkhoBMiQd3jgavJTUfAswIBnWwRcl8cGscDV0LtFvDJ5AwicHtSs7MphO6WkPNZ93YoAe90LgiOhaLLLJSsKRB31RJPpudewHkSk2HqHCW4a5c2oW804a2854vAB11pb4m0iYAuI3CvhaIi8FwkHjQ0EIQ3Am+gPHAnAjc0wP4IJpIZpLKF88CLEbUj8CXNQXS35lau/vWmk3DB6g6sXdKEkVgaADDlyULRkDUtz8U1kTHRhsBs31pJWMAZps5RQrt2SRM2HxguGYG7Kw/G0lkYOiEU0JFwCb3fQkmbFgK6BsAqmEbYECsx7clK96rLsXgGlkDBWijFUBZKZ1MQPa7SA6d0N+OC1bJrpLogePLAdQ1pUziTmADw+MFhBHQNrz57+czfWBnYQmGYOkfleq/pasLARApj8bTzXMqX7TDoqjwYT5kIaBoiAc2ztDvPQjEtBO1JzEYsZiWEnDyMBL0CPmz3A51OBH52bxvOXdWOM5a3ors15Gx3F71SF4RYKheBB3Syl9LnPudvPbAXn/+fHTN7UxXCAs4wdchILO0ItfKyVf6xSiUE8iPwwUlvBB4wSE5iZgtZKErARcG2YMkaWyjprOUpH1CMqVQWWUugIxqw7yQkQ1Py85tOBL6qM4pfvu/F6GoOoccW8KAhLRJFqEAWSkDX5EIeVwTeP5YoW69mtrCAM0wd8qE7nnHqfWdMC4amYZU9keZeHej3wN21v+NpU2ah+CYxHeskmxPygE4IGt4IXIl+rSYx797ahyu//nDZRTFj9gKb9mjQM4k5aNtJ04nA3SgLpcm3WlNlobjzwA2dZBZKykSrnY+ezFhlK0bOFhZwhqlDTkymMByTYiwjZMJKOwLfO5gT8JIeeCqLgE55eeD+FZiprPTAg/ZiFEVuErM2HvjARBKJjJnX4syPI+CRgMdCOXhC3qksbQkVPK4cykKJ+pbXq4h+ypOFoiYxs+hqzr1eao5TCVnAGaZG/POvd+Kbv99b8Lm0mYve0naWSE9LCIZG2GsvLgEKeOCT3ghcZaG4o1hlibi98KAhLZR0oWJWLg98W/84Nn7+9050Oxv+c/NhfOSOZ4o+r16/nA0xlpBWSXs06BHww7b90tNauA56OVQWir/gVc5CkQKu25OYlpDbupqCzr7prDWnC3pYwBmmRjy2/wQ2Hxgu+Jy7YUDWzhIxdA3L28MekfaL20Qi4yz7llkodhphoVooLgslqGu2heJeyJPf0OFLv92FE1MpPHkoV1hrpmw9PIo/7ine6DzhWtVYChWBd0QDjoXSGjYc62emAt4SMhAJ6E5qoUJdJNJZaT0RyYYOgKz62NUc9OyfmcNJYBZwhqkyQgjc/Mf9nlKkhUhlrKKr9dLZXASuJhkBYGV7NG8/N7F0Fh12pT0hZIpbJCgtlAd2DuDRfSecScy0ZyFPfgReqJjVjhcmAMCpvT0b0tncykXFTzYfxrb+cQCuei9lI3Ap4G3RnIWyskN+TgGdnNKx04WI0N0ayvPAZcQtPwBD05zXAeR31dnktWzmckUmCzjDVJlj40l88Te78PudpbvGp7LFJ7nSro4vaXuSEQBW2hOZRFJE/eIWS5nodN3CB3RZC8USwBd+vRM3/3F/rpiVbyl9yPB54L6l9KmsiWF7EctEMldffKakTQvJjOVcIDKmhX/45TZ87K5nYVkCiYzd3aZcBG6PqT2Sm8Rc1Sk/p+6WcNFeoJXwunNX4PLTuvO2q3xxFXm7s1+W+CJwv81VTVjAGabKKBEs1S0HkIJYSsBzEbKVi8DtyDIS0BEy9IJL6dtdEaehyzRCQNazTmWsvHrg6gIR0AnjiQy2vyAj4FweuNzvub5x57wTidITi5Wg3rt6nePjSVgC2HV8EvdtP+6yUMpH4E12DnjQ0NAU1J2L2EwnMBUfvXID3vXSdXnbVX9R9b240wzdF1BAfr67j09iLmABZ5gqk7W8S9WLkcpanl6ObtyTmBnTLeAysowEpGClfNkl6axVMAJ3Xs+0Ci+l1zV0NoUwNJnCtd9+FMmM6US+6v08e3TMOW81InB1AYnbk4FHR3M533dt7XNZKOU9cNWgoac1hNVdTU6E3NM6OwEvhspQMWwvKeDylDp8k6l3b+nDVd94GL/fUfqObCawgDNMlVECWS79LlUkT9iyBDKm8C20kQKhFvNEgjpCvrxtlW6nxAyAXcwq9988nbWQsbzdfJTH/vGrN+DdL1uHrN0Awl/MStXZbgkbmEhUUcBtoe4bkR1vTuqKYiyediLzshF4PI22iLzr+OiVG3D7TZvQHJKPZzqBWQ5/BO62UM5f3Y6wS8APDcsL05/2naj6OFjAGabK5NLzikfgQggks2bBCS6nTneJCDwaVBF47niV1tbpEnCVheKcO2t6LjBCCCQzJoJ23fDVXfICkcjILBiNZAQul6tnEQnoaI8GMJGsnoWiJjL7RuPQCFjf3YKpVLbyCDyRcfpUhgM62iIBNNuLabpnaaEUQ0X2uh15Ky8cAFZ3Rj2t2lojuVZv1YYFnGGqjBLIUisYM6aAEIUzLFJZ7wVA5WkDMqI0NEIkaMi2X64LgBI8twce0MizEjGVdXvgAgdOxBBPmzilW3aZURcKFW0rK8ISQCxtoimkozUcqEoErlIWnQh8NIHlbRF0RAOYSmYdD7xU1yFARuDtEa/vrLrzdM9RBK4i+0nbSlLZKKcvbwUReS6a6rN0W0TVggWcYaqMs1CmxCSmiioLWSjuBTxCCE8aoa4RVrRHEAloCBl64Qjc54F7I3DLEc501sKf98s89E3rugDAyeJQHrfq9J61LCTsglFtkUB1slDsz8At4L0dETSHDUymsq6FPOU98DZfqmCL44HPlYUiI3DVJ/MFu2P9xpO8FQsBmRsOAEdti6iasIAzTJVx0uJKROApn0i7cdsqygsPuG7R33DhSlx5xrK82iXKA+9o8lkortv5tGl5VmBuPjCMntYQ1tjWibICVLeZJrvJr2kJxFJZNAUNOwKfvYWi7jDUJGbfaBwrOyJoCRmYSmWd91PKAxdCSAvFJ+DnrmrHi07uwjm9bUWOnB3+yP415yzHxWs68b7LTgEAvPrs5fhf564AAOduJZExHTGvFizgDFNlshWkESoBFyK/2p87Ks+YMp3Qnab2t1esxzteslZaKK4slFgBDzygU14EnnVF4JsPjGDTui4nV1pF+iqaj9h1QLKWQCJjIhrU0RoxqhSBKw9cplMen0hiZUcULeGAx14qlQc+lcrCtESehbKiPYL/+ptNnotZNfFnt6xoj+DO91zq9M782yvW4yY7/dA9X7BvsLrphCzgDFNlcrVGSkTgLlHy2yjux2pBj7vKniJoaB4PXZU39XjgrjRC9/kA6cmemEo5t/1AzkJRjQkiduW9rCkj8KgTgVcvCyWRzmJwUuaA97aHnQlIRamVmGq1qz/3eq6pJL9czVu4P6vt9krWasECzjBVRlkUpRohpHwi7cYfgauelX5Chu7ZV0XgzSHDEWLD7omZG5twbJfRmF1DxOeZA7mLQdSJwGW7MBmBBxBLm2UXKpVDjSOWNp2xdDaFnIlTRakIfO+gLOy1bmnTrMYyXSpplKyqFk4kpcWzoi2Mxw+MVHUcLOAMU2UqyQN3C7g/wnQ3FlaedaBABB4yNKSystzq5V99CA/tHgQANIUMR/ADmncSE5C2DZCbXHNX21PHKV9aHWtasutNU8hw6l1PzjKVUF184qmsq6JgYFoR+D5bwNf3tMxqLHOBisAnk1mEAzo2revC5gPDVa1OyD0xGabKmBWsxCxlobgzS1RRq2ICnjYtHBmJ48BQDEeG49A1QsheUh5Lm0498EIo0XTXuw6odmF2ZoiK3rN2v8eIHYEDdmQ5C+vCvZDHXdPbXyirVAS+Z2ASve2RvKh9PvjZe19UstuPezVmOKBj08ld+PnT/dg7OIVTq3TBYQFnmCqjsk9K5YF7LBTTK1CpvCwU4REDRSggF/KoLulZS6A1bICInP0NXYOuUV6/SyCX3dHkFnDN64Gr6FxmoZhoCso8cGB29VCypgX18cTSptM+rj0ahP9jc39WP3+qDw/sHMSFJ3XgHS9Ziz0DU1jf0zzjccyGC11zB4Vwf2chQ8Oldqrm5gPDVRNwtlAYpsqYvqbBhShpoRSYxDQK1G9VonxiKle2VkWiSjyUJfKmi1biqjN7Co7F7ZEHDDuN0MlC0Z33IrNQDE8EPlPcdyeJdNaJwN2rKBXuCPzWPx3Er54/hq//fg9MS2D/UPWi2WrjnngOB3Ss7IigKag7nYKqAQs4w1SZXCOGUhF4ZVkoqayJrCUKWygB3ROBA9L/BvJrdHz+urNxzVnLC45F5Xq794/bk5jKA59M5SJytTR8OpkoQgg8fSTXBMIz+Zo2PRUF3XaIRkDSXaPcFvPJZBbP948jnbWcVaT1hlvAIwEdRIRoyEAyY2IklsahKgg5CzjDVBnTqmASM1NZForyogtZKCoCH57KF/BcFkruuELnALweuDoulvZOYiqxjoYMZ3XmdCLwu7b04fXfeQz3bT8u36OnBEAWo/G0U4TLLeBtkYBnviCZsRxb5w+75KRtvUbgmqvxQ9hOx4wGdcTTJv7l/t14xw+fnP1rzPoMDMN4cBbyVOyB+7NQXOLmapzrJ2RoMC2Bwclcf0olfmpyzV3mtFAuOeDPQlEeuOl5Ti1GaQrqTqW9cp1y3PTbS8232912Mh4BNzEezzj567pGThecjmgwLwJfb0fcD9oCvr5OI3Ag95mrieRIQAr48FTK0xpvprCAM0yVydVCmb2FooS0sIUitylxBHJ2SM4DLx2BB+1Wagqnn6Z94Qj7I/Cg7mwr1ynHjVrqrmqHeN5jSloo7gVIygdvj/ojcBMn24L9fP84etsjzl1HPaI+c0fAg7LBdDxtYiqVnXUuPQs4w1QZd4uwYpSaxHSLu7IyCgl4r90f89mj487SbpVREnAt5FG4BVwJdTTkTTE0fBaKsleUXRK1qyAC5et0u1GNgUftbBP12ch0R9tCcS2HV3cSnU1Bz+eTzFpY3hZ2IvRaZaBUilrw47dQ1IVZTRbPlIoFnIh0InqaiP7HfryWiB4non1EdAcRze9aVoapU5xJzJJL6Sv0wFMqAs+3UJR4TaWyOLu3HYDLAy8TgSthjvpyxIO+SUxloUy6ilsZugZDo7JVAt0owVaLh5Qot0cCSPgsFCBXBbEtEnQi8Iwp+2eGDd1pbFGv/rciLwIPGIinTecOZ7ZFwaYTgX8QwE7X4y8B+LoQ4hQAowDeOauRMMwCQaURlvbAK7NQYqniEfiariYnvbC3PYy3vWgNrjhdNuAN6gUEXHcLuBSUqM9+UBeKqaIWSs5jn04Ert6TPwJvjwYwlcrmWSgtYQPhgIZIUHM8cGXZqJQ8oL79b6C4haJK5c62KFhFAk5EKwG8BsD37McE4HIAd9u7/AjAdbMaCcMsEHJphBVaKL793I9LWShBQ8PaJbIGSGdTCJ993Zl4xYZu5znAa6Go23ggl9/dFPRG4LpGIFe3e3WMmsRUwh8O6NOKwB0Bj3k98PaotEj8FQWbQwYiAR1hQwre1sOjzhjCAc1p7lz3EbiaxLStlGhARzydde6s/KmYR4bjTm3xSqg0Av8GgI8DUH9ZXQDGhBAq/u8D0FvoQCK6iYi2ENGWoaGhigfGMI2KWeEkpoqeS05ipopPYgI5Aetq9jqYjoWiuSPwnFgrIY74BJyInNdyT3D6I/BwQJ9WBK4uCMpCURe5FW25utruCHztkias7mpCKKAhnjZx/Xcfwy+e6nNe+6zeNrRFAnXvgecicPkzYnvgCfvC7I/AP3rXM/jMf2+r+PxlBZyIXgtgUAixteKzuhBC3CKE2CiE2Lh06dKZnIJhGoqMY6GUzgNXbb8KCXjI8E4mBo18DxzI+eBdvpokjoViFJ7EVPnd7mX0/mODhub0fFRCo7JcQr5StuVQ73EqlUIu8sEAACAASURBVEUyYzoWyvUXrnT2cTdj/uiVG3DXuy91IlcA6B+T6ZLhgI7rL+jF5k9d4clhr0cKWSiJtIm4slB8HvhoPDOtlZqVROAvBvA6IjoE4HZI6+SbANqJSH16KwH0V/yqDLOAMSuZxMxazkRdIQFXWRil0ggB4LRlMgL316d2LBR3BO4WcDWJWSAFT9kuAZ2cuwQVgStBDQX0aaURunPbhyZTjvh3NgVx7Xmyc02rawm9rsl6LiGX7TNslwwI26sa/XcP9UjIF4FHA7rdJFo+74/AE2kT/aMJWCX+dtyUFXAhxKeEECuFEGsA3ADgD0KIvwLwIIA32LvdCOCXFb0iwyxwKs0DjwZ1GBrlF7PKWk42yVSJSUwAeOXpPfjWX56PC1Z7CyvlJjELR+Aq+8SfheJ+rYCei8Ank1lEgzo0+7GcxJy+Bw4AAxPJXBqhruHLbzgH37zhPFy0pjPvOHclxWG7ZIDby6938hby+C46fg88mTGRNi0MTVW2yGc2n8QnAHyEiPZBeuK3zuJcDLNgyFZYzEqVfS1moQR0cqoCFkojBGTe9uvOXeEIq6JgGqE7CyWke356jnVZKI4Hnsx47IpwwGuh7D4+ic/du6PoxG3KI+Ap5z0HDdmc+drzevPeAwBPuVZ3BN4o+C0Uv+Xj75GpslOOjlTWwX5aAi6EeEgI8Vr79wNCiIuFEKcIId4ohJj9ulCGWQCYlZSTzVgIGXpeWzRA2g2hgBTPcpOYxSi0kMd9EYgGi3vgar+gKwJPZixP0auQoXtWSH7vkQP4/qMHcceWowXHk85aaLOrGB4eiTkXt3Lvy/28qvkSrqAbTr0QyvPAve/X3S9TCOEIeN9oZZkojXMvwjANgsqwyJgWDp6I5UVTj+0/gYlkBqGAhqCuYTiWxt1b+3Dvsy/AsgTSWdkDM2hoZS2UYhTKQiEiR1AiRW7p3a8VNDRPGduWcPEIfLmdTXLzH/fjjieP4M4nj3rK3KZNC51NQSxvC2PvwJTjiRcrsKU4PpGr8zLplLhtHNlyInDnc/deMMfiaTyydwhCyFZ3yhvvG60sAq/vKVyGaUBMV1PjT/zsOYQMDT955yUAgL0Dk3jzfzwOAHjl6d0IBTT86rlj+NVzxwDIW+h01nLsi3KTmMU4qSuKzqZgnkWiIn41ienPA3e/ltsDB7xVAkOGdxJT5a4fHUngEz97HgDw7pevw6euOR0AkM6aCOoaVnVGsWdgEmeuaK3ofZ2/qsMZt7JdKulHWS/kWyi5sQd1DQ/uHsKDu4dw7/tfgtX26lKAI3CGqRnKHsiaAuO+tLDH9g87v4cM3fGb26MBnNXbim//YR/imSyC9nNOGuE0Bfw1Zy/Hk59+ZZ7YqQi82EpMIGehyCyU3Os2h3J52v4IPJWx0Bo2sOUzr8Rjn7wcnU1BT89MdVE6tbsZ+wannGNLtSQDgEtP7sLeL1zjdLORr91AAq5773Tcdzw9bbnMoeMTScc+AVjAGaZmuOuBJ7Mmjo0nncm9zQdyAm7ohKAtsKs7o/jQFafiyEgc2/onHAtF3VIHiuSBF4OIPNGzQl0ISnvgrjxwl2/uTvPLi8CzJkIBHUuaQ1jRHkEk4H/eFvCeFqSyFvYPTXleqxQB3dvkoaGyUBwLJVdOVrGsNbeIaXgq5Qi4rhEODVeWC944nwTDNAgqjdASMo/btASOTyRhWQKPHxxxVhweGIo5/8FXdkRw+WndzkSfykJRuCPh2RD0eeDRMhaK2wNv9nng7pWYyYzlEdaw3a9ToTJr1MKj7f0T0AgFLzKFcE+gNlQE7s8Dd33ehNx7H46lkbDtsgtWt6NvNIEBl/9fDBZwhqky7lQ6VYyqbzSBvYNTGIml8TcvXQcAOHQihqAt0is7otA0wiVrZS50yNCKpgDOhqBjodgLeQoJuL1PyDeJ6Y2CZS0UYd8ipLKmx64J+yLwtCkjcNX+bPfAZNkJTDdqvLpG054PqCXKIgoVSCMcS+Q6KQ1PpZ0IXNWzcd+tFaNxPgmGaRDcKzDVJGTfaAIHbNvgFRuW4t0vX4d/f8uFTh6wqq63yfZ6T8TSjsAFdc0Tgc4GJbIXre3AdeetwJm9bXn7BB0P3DeJ6bFQNFgil3GTH4HrSPoqLgZ1DS3hALrtVaPTEWJ18Yg0UPQNyO/6rZee5NhP7vH/yxvPxV9dshq97RGMxFLOBe/81e1oCRss4AxTCwqtwOwbjTsrCZc0h/Cpa07Hi05Z4rTVWmVX17v0ZCngTx8edQRu3dImT2/L2aAuCkubQ/jGDed7omqF20Jxe+lq6T+QszFURcJkxvTkZ/stFjWJCcCp5V1uAtONWpnaSP43AJy5og2fu/YsyAKuuUnMcEDDOSvb8YXXn42lLSGPhdIcMnDJ2k5sPjBS9vyN9WkwTANQaAFP32jC6R7f4SraNBb3RuAb7OqCb9602hG4apZMlRUGyRGUQhiuSUwg51O3hLwROJDrypPKWp66JZGA7giSet7t9wPTjcBVEa3GisD9qNx69+RxV1PQY6FEAjrOX92BgydiZTv2sIAzTJXJFKhC2Dcax/BUCq1ho6D322uLmqYRDvzzq/Gpa0538p5PrWLJVCkg5VY/5iwUAI4P7skDLxOBh/wWipmrsKgEXCtxEfGTK2Pb+JIVCeie/PzOpiBGYjkBDwd0p7qkv1aKn8b/NBimzigWgQ/H0uhq9lYNVBkp7sktVRPkiL2C85TuKkbghuZZXl9wHz03iQnkInC/Bw4Uj8DDhp6XhaLOq5oxqO48lZCzUBo7AgekjeKJwJtDUsDtO5ZIUEernY1UrmMPr8RkmCrj98B72yM4Np7EirZUXt3u33zwpTgxWVjIjo3LNLJqR+CV1h9RkbjheODeLBQg1+assAfum8T0ReDxdOXVDJsXkIBHg7pnQU9XUxBp03JKD0QCOlrt+YZyPTM5AmeYKvDAzgF85b5dAPIbOazvaYZpCew8NoFOn4Avb4vg7JX5mSBu3EusZ0vIl9tdiECeBy5/trhWYqoIXK2oTGZ8EXjeQp5cmqGKwKeDysJptCyUQkSChicCV38T/fbqy3BAR2tEPl/OQuEInGGqwMfvfg7DsTResaEbWUvA0MhJJzxlaTMe2j2EyVQ2z0IpxQ/ffhF2HJuoWgYKAFx7fi9OW17akvF74Opxc4EIXFUk9OeBRwI6klkLQgiYloAlcheEFe25FYiV0tygWSiFuPHSkxxLCMi1w+sbTTgVINvYQmGY+eP05a34074T+Mbv9yBrCoQDupNB4O7b6LdQSvGKDd3Ooo5q8fJTl+Llp5ZubehOIwRcHniBLBQVgacylsfeCAc0mJbA4eG4U1Ew6CwQmn4UrWq2hBZABH7Dxas9j7ua5EW9fyzhXKByFgoLOMPMOWri8tF9w2gJGwgHNKhqqmuXNIMIEAJ5Fko9EvBNYhoaOc0nFG4P3LKEJ8vE/fyXfrsLD++Rzczdq0l72yNY1RmpeEzNKgulwdMIC7GkRf5NHBtPOK3x1HyDu154IVjAGWYWWJYAEZzOOYBsP9bWkROnlrCBZa1hHBtP5nWPr0dU4Sx3BO6ewARy4h5Lm066oDsCV5HyC2MJxOzJSvcF4NFPXj6tMSkPfCFYKH66W8II6ISMKRyP39A1NAX1vI49fljAGWYWXPmNh3HDRavyFlx47QQdKztkJoq6Xa5ngr5JTEPT8lZsqvf3sbuexX3bjwPwrqxUDQzUSlP3+WaCoWtoDRuON7yQ0DXCivYIDg/HPX83rZEA54EzzFwhhMDBEzHsHZhCLGV6OsN77QTNybxoJAvFG4F7hdMtNNv7x/O2qd+HXAI+naXzhbjtXZvwLrsQ2EJDpVa60wtbw4Gyk5gs4AwzQ1JZC6YlMJHMIJbKoqc1J+DudLeIHYEDwJIGsFAMV0MH9dgfgbvFWEXZ7m3q/bsLe822ouLZK9sa4gI4E1a2ywt8xBOBG2XzwNlCYZgZolbOjScyiKWz6GkJYxsmAORHo687dwWSGRNLppFGWCv8k5hvvXRNXtlZt1grkS4UgbuZjYWy0FEXeI+FEg54eoIWggWcYWaIanc2OJmCJYBuV4cV92SbbGTQgk+/5ox5H+NMCPoslDdcuDJvn0K56f6GDn4avRDVXLLSzsiJ+DzwPYOTJY/jSyLDzBC1FPzYmFxB1+32wAOqep5WsvJfPeJfiVmM5pDhWdXpb+jghyPw4qg5Em8EXt5C4U+UYWaI6raj0uR6XBG4iqQasXaHfyVmMbb941X40vXnOI/LReAs4MXJTWLmPqPWSACTyQysAsXRFPyJMswMSfiKMXU2BaECUiVgjZi3rDJO/LnfhVDVFAFvBF7ILqlWW7iFSE9LGE1BHe2R3CRtazgAS+SsukKwB84wMyTmE/DmkIGmkIHJZLZgF/JGYdO6Ttx+0yacuaJ0kS0AaHc1p3BfrNzpcBrBUwuFyUfTCHe950WeOjFOQasSqzH5E2WYGRL3RUZNIT2v7GkjWihE5PTmLEexCNz9vlWzitnmgS90zljR6rkgqkVLN37/iaLH8CfKMDPEX89aReBALhpdCMWXStEecffJzF+JCQBrupoAcAQ+XTau6cRrz1mONV3Fy+/O6yc6WaYwC8M0ErGUPwI38jrHhBe4aLmXtrsvVoar7vjaJbaAswc+LZY0h/DtN1+A7914UdF95vUTLVeYhWEaCX8E3hQy0GR7v6EGtlCmg6FrzmSn3yIJB3RoBLz4lCU4eWmTp544Ux3m9RPNmPnNXhmmUfFnBzQFdScCz6URLvyosyMaxGQyW0DANehaAFeduQxXnbmsRqNb2MzrX5fqss0wC4FE2vSkDRp6rmpf0NBA1JhZKNOlPRoouGDJ3RqMmRvmVcAzplWwYzfDNCKxVK62iepxqOpWGxrB0GjBWyiA9MELvc+wqzkvMzfMq4ALAIOTpYuzMEyjEE9n0RoJIOqyTtRPKeDaohDwjmiwYIpgOKBVtBiImTnz/un2jSawvK3yVkoMU6/E0yaagjraIoGcgNuRuKETPnH1BlxwUkcthzgv/NUlq/Gik/Pzxt/z8pMXhYVUS+ZdwI+OxHHRms75flmGqTrxdBbRoIHWsIVm2zrJReAa3vbitbUc3rxxybouXFJg4c9rz1lRg9EsLmoSgTPMQiCWMrG8LYCTuzucyLvZ5YEzzFxTVsCJaBWAHwPogbSxbxFCfJOIOgHcAWANgEMA3iSEGC35YhqhbzQ+2zEzTE1IZy2kTcvJNElkTERDBj5/3dnOPioC11nAmXmgkknMLICPCiHOALAJwPuI6AwAnwTwgBBiPYAH7MclCRoaR+BMw/LV+3fj+u885jyOpbLOwh3FUjsrpS3K2RfM3FNWwIUQx4QQT9m/TwLYCaAXwLUAfmTv9iMA15U7V0DXMFCmRRDD1Ct7BiaxZ3DSWc8QT5ueqnsAcPHaTtz7/pfgtGWttRgis8iYVhohEa0BcD6AxwH0CCGO2U8dh7RYSmJo5DRAZZhGY2AiBSGAY+MJCCEQS2cd71tBRDh7ZfkyrAxTDSoWcCJqBvAzAB8SQky4nxNCCEh/vNBxNxHRFiLakk4mMJnM5pXhZJhGQK1h6BtNIJW1IAQQDXGaHFM7KhJwIgpAivdtQoif25sHiGi5/fxyAIOFjhVC3CKE2CiE2NjW2gwAGJzIj8K3Hh7BU0dKzoEyTM3ImBaGY2kA0kr51gN7AQBRznNmakhZASdZ4OBWADuFEF9zPXUPgBvt328E8Mty51I99grZKP/0PzvxD7/cXn7EDFMDTkxJ+wQAvvfIQXznof1oCurYwF43U0MqyQN/MYC3AHieiJ6xt/3/AL4I4E4ieieAwwDeVPbFNCng7onME1MpdEaDODGVwvBUGpYloHEKFlNnDLjuGvvHEuiIBrD1M6/iv1WmppQVcCHEnwAU+yu9YjovprpdKwEfmEjipV9+EF95wzkYnkojkTHRN5rA6hIdKBimFgzaf7MtIQOTqSwuWdvF4s3UnHktZqVrhKChYci2UP68fxjprIXtL0wgkZHF8fcMTM7nkBimIgbsv9nzVrcDkI1/GabWzHu1+e6WkBOBbz4wDADYeSyX1LJ3cGq+h8QwZRmcSEIj4LxVUsAvPXlJjUfEMDWohdLTGnYmMQsKOEfgTB0yOJHC0pYQ3rRxFaJBA6f2NNd6SAwz/xF4T6uMwI+NJ3BoWNZFOTEl07Nawwb2DLKAM/XHwGQS3S1hrOqM4r2vODmv+wzD1IIaWChhDE6knKh7Q0+L89zFa7uwb3AKFnftYeqMgYkUelpDtR4Gw3iYfwFvDWEylcXBEzL6PndVbtnxpSd3IZmxcJQrFjJ1xuBEEt2t4VoPg2E8zL+F0iL/E2zrHwcAnLlCCnjI0JwJoj0DPJHJ1A9qFWZ3C0fgTH1RAw9cCvjz/eNY0hxEb7tsr9bVFMR6e2JoL/vgTB2h0l57OAJn6oyaWCgAsH9oCktbws5/is7mIFrDASxvC2MvR+DMHPP4gWG85dbHkTGtsvuqtFf2wJl6o2YWihDyP4QS9K4m+XN9Twsv5mHmnMcPjuCRvScqajCi0l67WzgCZ+qLeRfw1oiBkCFftqcljK6mIDSSFgoAnNrdjH2DUzBdmSiP7T+Bx+2ccYapBhOJDABU1OJPLaPv5gicqTPmXcCJyPmP0N0agqFruObs5XjxKXJl24UndSCVtXD/9uPOMV/6zS589Xd75nuozAJmIqkEvHwEPjCRgq6Rc5fIMPXCvAs4kLNRVFrWv735Alx/4UoAwJVnLsO6pU345gN7nXzwE1NpTCa5CQQze8biaYwnMphIyL+nQhF4xrTQP5YT9sHJJJY0B7lRMVN31EbAbeHuKZCWpWuE973iFOw6Pomnj8oGD8OxFKZSmXkdI7Mw+dvbn8Enf/ZcyQj8ri19uOxfHsLwlPS+5SIe9r+Z+qMmAr60RVkohf9TXHBSBwDg4Ik44ukskhmLI3CmKvSPxnF4OF5SwA+emEI6a+HxgyMAZBYKT2Ay9UhtI/Aik0Ir2sMgkre3w3adlKlkFkLwEntmdkwksxiOpUpaKKp5w5/3y4nzockUT2Aydcm8VyMEgOsv6EVzSMeyIhF4yNDR0xJG32jC6UOYtQRSWQth7kHIzIKJRAaWEIja3eQHJlJIZU2EjNzflWpevPmArFc/HEs78zYMU0/UJALvbg3jLZeuKVnRbWVHBH2jcYzEcq2s1G1vIY6PJ/GDRw9ylM4UJZkxkcpayJgC44mMswr4M7/YhuPjSTy4exCP7TvhNN3eOziFXcdl0TVexMPUIzUR8EqQAp5wLBRA2ijFuOfZfvzjvTtwbDxZdB9mceOfR3n5hqVY1RnBXVv78Jttx/Dl3+7GF3+7CwMTSZyxXDYrfmj3EADOAWfqkzoW8CiOjSc9HeynUsUFfDQuo/NCHe8ZBsi/gzuntw0P/91lCOiEwckUBieS2HVsErG0iYvWyIn0rYdlJhRPYjL1SB0LeASmJbD9hXFnW6kIfCwuI3V3x3uGcaNWXypaIwG5sMw135K2a6OcvbIdhkZ46ogUcE4jZOqROhZw2Zn+2aM5AZ8oKeAcgTOl8f/9tIYDAKQ9sr1/3PPcirYwVrRHMJnM2qswg/M2ToaplLoV8FWdcoKpfyyB9qj8j1bKQnEEnCNwpgj5EbjMROlpCePAiZjnue7WMFZ2yL/Bpc0haLwKk6lD6lfAO6JO49hVdjQ+VSILZZQtFKYMfg/cHYH76W4NOQLOGShMvVK3Aq5phA9ecSoAOJklKovAsgS+/Ntd+Pjdz+KZo2MAgPEEWyhMadTiHSXIrZGA/Tjnb5+2rAWRgI6WkOHYeEt5ApOpU+pWwAHgmrOW4cozevB/Xns6QobmWCjP9Y/jOw/tx51b+vCDRw8CcEfgLOBMYSaSGQR1DSvs/O+WsLRQVKs0XSO848Vr8foLekFEHIEzdU9dC7imEW5560Zce14vWsIGJm0B32zXBj+rtxV7BqaQzJhIZmT2AHvgDCDF2l1REJB3aa0RA11NQUSDOgK6/PNXNXmWNofwpotW4Z9ffzYAYFWnjMA5A4WpV+pawN00hwwnjfDP+4dxSnczLl3Xhf1DUxixl9t3t4QwHEtX1CaLWdh89b7deNPNf/Zsm0hk0BoOYN3SZmdeBchF2P5Ie+2SJgQNDad0N8/9gBlmBjSOgIcNTCYzyJgWthwawaZ1nVjf04J01sJzfdIH37CsBUCuCS2zeDk0HEf/WMK5uAMyjbAlEsBHXnUq7nzPpc52f316xZLmEP70ictw9ZnL5mfQDDNNGkbAW0IBTKWy2Hp4FLG0iU3runBqjxTsJw7KxRbq8XG2URY9KhvJ3V9VRuAGwgEdbfYEJgC0RwMIGVpBr7u7JcwphEzd0jACLiPwLG7+4350RAO4bEM31tu3tk8eknWbVf2KY2Ms4IsdlY201y3gyYyTeeKGiPCtvzwf73rJunkbH8NUg5qUk50JLSEDB07EsOv4JD5x9WloCsmh97ZH8Ly9iu6s3jYA3hrPTx0ZxZ7jk7jh4tXzP2hmzvjttmMwNA2vPKMn77l01nKsk72DU872iUTWyf32cxXbJEwD0jAR+MtOXYoVbWFctKYDb730JGf7JWs7nd9XdUbQFgl4uqz84NFD+Md7d3CZ2QXG1363B//8650Fnxuays2BKAtlLJ7GcCyF3nbOKGEWDg0TgV93fi+uO783b/t7X3Eyfv50PwAgEtCdOuKKvtE4EhkTI7E0upo5n3c+2fHCBNb3NDvpetVCCIG+0QTiaRMDE8m8ND/lf3e3hLDr+CQe3jOEeNqEEMCmdV1VHQvD1JKGicCLsb6nBacta4GukbP44qgrAlfReKHeh8zc8cJYAq/910fws619VT/3aDyDeNoEkFsT4EatBXjVGT0Yi2fw1u8/gS/+ZifCAQ3nrGyv+ngYplY0vIADwD3vfwme+j+vAiCrGPaNxiGEQDJjOimFLODzy85jE7AEsO2F8fI7T5OjI7k7rIICbn/nH7xiPe7/8MuwoacFh4bj2HhSJ4LGgviTZxgAC0TAg4bmpIWt7IggmZF9DN2ifbRA81oAeGDnAK746kOIlah0yEyfPQNy8nCv/fNr9+/G9d99DKY1+7kI9b2u7Ijgp08cxYu/+AfE01mksxZe+6+P4B/v3QFdIyxpDuHUnhZ88JXrAQCb1nWWOi3DNBwLQsDdqBV2faOJPC+8EPc++wL2D8Wwxe68wlQHlb63d3AKAxNJ3PzwAWw9PIp7n31h1udW3+U3bzgPf7FxFfrHEth6eBR3bjmKbf0TMC2BgE5O/vbVZy7Dl68/B3+96aRSp2WYhmNWAk5EVxPRbiLaR0SfrNagZsNKu474F361Az97Sk5uLmkO4vn+CXz2nu0Yi6fxrQf24n23PYWfP9WHzQdkDvkje4bwhV/twIGhqaLn9jMSS+Oz92zHeLx4mdvFikrfG4ml8flf7YRlCZzUFcW3Htg77Sh8z8Ak/u9vdjrH9Y0m0BYJ4MKTOvH3/+sM6Brh4T1D+M6D+7BuSRMAOLVxAFlT500XrUJ7lJsyMAuLGWehEJEO4N8AvApAH4AniegeIcSOag1uJqxd0oQXndyF5/vH8eShUQR0wvmrO/C7HQN49ugY9g1O4U/7TqApqOP+HceRMaUo/OjPh5AxBQ4MxXDr2y6q6LX+9Q978cPHDiEc0PHJa06bw3fVWFiWwL7BKZza04w9A1O499kX8BcbV+EVG5bivbc9hXue7cfrz19Z8fk+e892PLZ/GKcva8V15/eibzTuVApsChk4Z2UbfviY/P5+/I6L8eShERZrZlEwmzTCiwHsE0IcAAAiuh3AtQBqKuAhQ8d//c0m3L/9OG76yVasaI/gJLuqXFDX8Kd9J7CkOYgfvO1ivO7f/gRAlq39zbbjCOoaHtg1iB89dgidZVpoZUwLtz1+BEFdw4//fMjJhGGAsUQGiYyJ15y9AnsG9sDQCO+//BT0tkdw2rIWfPP3e2Fold38DUwk8dj+YQR1Dd98YC90jbBnYApn9bY6+2xa14Wnj4zhgtXteOn6JXjZqUvn6q0xTF0xGwHvBXDU9bgPwCX+nYjoJgA3AcDq1fO3GvJVZ/TgvFXtWNYaxhkrWhEOaPjuX1+Id/9kK9532Sk4e2Ubrr9gJZ44OIJ3vXQt7tt+HN/6y/Pxmf9+Hv9wz/aKXiNoaPj3t16Im368BR+645k5fkeNx6vO6MGdW47istOWOqVZP3rlBvzNj7fgAz99uuLzLGsN49OvOR0f+OnTznHXX5BbE3DZhm78+x/346NXbgARX0SZxQPNdIUiEb0BwNVCiHfZj98C4BIhxPuLHbNx40axZcuWGb3eTEikTRDJyHsqLZdRj8bSaI/KbuQZ00Iqa6E5ZGAsnkZ7NIjRmFyxVwmtkQC6W8IYnEjmteta7ESDht0UOINIQIfhWsxzdCSOVNas+FxLm8NoiwZcxxHWLmny3PGo749hFiJEtFUIsdG/fTYReD+AVa7HK+1tdUMkqDu/qxoYHS5rJKBrzipB9Z+/oyno2acSulvDeaVIGUlLgdojKhqfLqWOY/FmFiOzyUJ5EsB6IlpLREEANwC4pzrDYhiGYcox4whcCJElovcDuA+ADuD7QojKzGOGYRhm1syqmJUQ4tcAfl2lsTAMwzDTYMGtxGQYhlkssIAzDMM0KCzgDMMwDQoLOMMwTIMy44U8M3oxokkAu+ftBWdOG4DqF7KuPjzO6sLjrC48zuqxQQjR4t843y3VdhdaTVRvENEtQoibaj2OcvA4qwuPs7rwOKsHERVcws4WSmHurfUAKoTHWV14nNWFxznHzLeFsqURInCGYZh6oph2zncEfss8vx7DMMxCoKB2zmsEzjAMw1SPBe+BF2r7RkS3EtGzRPQcEd1NRM1Fjv2UfdxuIrqq1Dnnv3vjyAAAChBJREFUaJxERF8goj1EtJOI/rbIsTcS0V77342u7RcS0fP2Ob9FVSiWXWSclxPRU0S0jYh+REQFJ8fneZzfJ6JBItrm2vYVItplf++/IKL2St+jvX0tET1ub7/DLuI2F+P8LBH1E9Ez9r9X1+k4zyOizfYYtxDRxUWOnZfvnYhWEdGDRLSDiLYT0Qft7W+0H1tEVNTCnc/Ps2oIIRbsP8giW/sBrAMQBPAsgDMAtLr2+RqATxY49gx7/xCAtfZ59GLnnKNxvh3AjwFo9n7dBY7tBHDA/tlh/95hP/cEgE0ACMBvAFwzR+M8CuBUe5/PAXhnLcdpn/NlAC4AsM217UoAhv37lwB8qdL3aD93J4Ab7N9vBvDeORrnZwF8bCbfxTyP8371XQF4NYCHavz3uRzABfbvLQD22H+fpwPYAOAhABvr4fOs1r8ZR+BFIrGKrlQ0f5Gt0/ZNCJEGcDuAa4UQE/brEYAIgEI+0rUAbhdCpIQQBwHss89X8JxzMU4A7wXwOSGEBQBCiMECx14F4HdCiBEhxCiA3wG4moiWQ16oNgv5l/djANfNwTivB5AWQuyx9/mdva2W44QQ4mEAI75t9wshsvbDzZA17P0U/C7sv5XLAdxt7/ejuRpnhdTDOAUA1duuDcALBQ6dt+9dCHFMCPGU/fskgJ0AeoUQO4UQ5dafzOvnWS1mJOCUa2h8DeQV7i+J6AzIqObrQohTAIwCeGeBY8+ArB1+JoCrAXyHiPQS55wNhdq+9drj+AGA4wBOA/Cv9rbXEdHnyhxb9JxzMM6TAfyFfXv6GyJab49zIxF9r4Jx9s3DOJcBMFy3pm+A3eijhuOshHdARn0gohVEpKpqFhtnF4Ax1wVgrsf5ftvq+T4RddTpOD8E4CtEdBTAvwD4lD3Omn/vRLQGwPkAHi+xT719ntNmphF4sYixkivVfEa2RRFCvB3ACsir9F/Y2+4RQvz9XL3mDAgBSAqZPvQfAL4PAEKILcJuZVcHCMgL8teJ6AkAkwBMoO7G6UBEnwaQBXAbAAghXhBCFPSZa8R3IS/e5wE4BuCrQF2O870APiyEWAXgwwBuBWr/vZOc0/oZgA+pu+1C1OHnOW1mKuDFrlYFr1Q1jGxLtn0TQpjI2QCVHjsXreSKnbMPwM/tbb8AcM40x7mywPaqj1MI8WchxEuFEBcDeBjSe6zlOItCRG8D8FoAf2XfuvspNs5hAO2Um6Cds3EKIQaEEKZtnf0HZHBTd+MEcCNyf593TXOcc/K9E1EAUrxvE0L8vNz+FYxzPj/PaTMvWSg1jGwLtn0jolMAxwN/HYBdBY69B8ANRBQiorUA1kNOusxFK7li5/xvAJfZ+7wchYXxPgBXElGHfat9JYD7hBDHAEwQ0Sb7fb4VwC/nYpxE1A0ARBQC8AnIiZ5ajrMgRHQ1gI8DeJ0QIl5kt4Lv0Rb7ByEtIkCK11yNc7nr4esBbCuwW83HCel5v9z+/XIAewvsM2/fu32eWwHsFEJ8bZqH18PnOX1mMvMJ4FLIL0E9/pT97wRys/yeffz7uh7fZ+9b8JwzGZ/v9V4NKXz7AXwa8qL1KIDnIf9j3AY7KwVSzD/nOvbT9nG74Zoh959ztmMsdk4A7QB+ZY/1zwDOtbdvBPA917HvgLSi9gF4u2v7Rvs97gfwbdh5/3Mwzq9AWlG7IW9bUQfj/Cmk/ZCBvJN5p/26RwE8Y/+72d53BYBfl/t+ITMUnrDPcxeA0ByN8yf2d/4c5IV8eZ2O8yUAtkJmbDwO4MJafu/2eIT9uanv+NWQF8E+ACkAA7B1ppafZ7X+zWghj307sQfAFZC3E08CeDNk+tPPhBC3E9HNAJ4TQnzHd+yZAP4L8nZrBYAHIKNbKnROwX02GYZhCjIjC0VIn1s1NN4J4E5baD8B4CNEtA9y9vZWwOuB2/vdCWAHgN8CeJ+Qfl+xczIMwzAF4KX0DMMwDcqCX0rPMAyzUGEBZxiGaVAqFnAqXMym0iIxPyRZnCdkP15CRIdmNXKGYZhFznQi8B9CLn13sw3A/4ZcvFEOEzKdiGEYhqkCFQu4KFwcqJIiMYpvAPgw+UqNkuQrJEuRPk9Ef2Fvv52IXuPa74dE9AYwDMMwAObXAz8C4E8A3uLb/r8haz6cC+CVkMVxlgO4A8CbAMBeGXUF5KIWhmEYBvM/ifl/Afyd73VfAuCndi74AIA/ArgIslLcZbZvfg2Ah4UQiXkeL8MwTN0yJwJORD8g2aXj1+7tQoi9kMtb31TuHEKIJGQB9qsgqwXeMQdDZRiGaVjmRMCFEG8XQpwnCpdq/AKAj7kePwJZ81onoqWQnT+esJ+7A7IrzUshV20yDMMwNtNJI/wpZEGlDUTUR0TvJKLXE1EfZCGqXxHRfeXOYy+Pf8q16ReQxWeeBfAHAB8XQhy3n7sfstrZ74WsEc4wDMPY8FJ6hmGYBoVXYjIMwzQoLOAMwzANCgs4wzBMg8ICzjAM06CwgDMMwzQoLOAMwzANCgs4s+ggojVE9OYZHDejgmpE9DYiWjHd4ximHCzgTEPjr25ZIWsgm3DPF2+DbODNMFWFBZype4jorUT0HBE9S0Q/sSPhm4nocQBfJqImu+HIE0T0NBFdax+3hogeIaKn7H8vsk/5RQAvtev1fNgu4/AVInrSfp1328cTEX2biHYT0e8BdJcZ59/b59hGRLfYx78BwEYAt9mvF5m7T4pZbPBKTKauIaIzIcstvEgIcYKIOgF8DcASANcKIUwi+mcAO4QQ/0lE7ZC1dM4HIABYQogkEa2HrHq5kYheAeBjQojX2q9xE4BuIcTn7eqXjwJ4o32O90I2MukBsAPAu4QQdxcZa6cQYsT+/ScA7hRC3EtED9mvt2UOPiJmETOT20+GmU8uB3CXEOIEAAghRogI9jbT3udKAK8jIlUkLQxgNYAXAHybiM6D7Ah1apHXuBLAOS5/uw3AesjCaj+1X+cFIvpDmbFeRkQfBxAF0AlgO4B7p/VuGWYasIAzjUrM9TsBuN7fHYqIPgtgALJZiAYgWeRcBOADQghPMTYiKlRNs/AJiMIAvgNgoxDiqP3a4UqPZ5iZwB44U+/8AcAbiagLkDZFgX3uA/ABskNzIjrf3t4G4JgQwoLsBKXb2ycBtPiOfy8RBezjTyWiJsher6rU8XIAl5UYpxLrE0TUDMCdreJ/PYapChyBM3WNEGI7EX0BwB+JyATwdIHd/gmy5+pzRKQBOAjgtZAR8c+I6K2Q9eRV1P4cAJOInoVs1v1NyMyUp+yLwBCA6yC998shve8jkOWUi41zjIj+A7LR93EAT7qe/iGAm4koAeBS7izFVAuexGQYhmlQ2EJhGIZpUNhCYZhpQkS/ALDWt/kT/klQhplr2EJhGIZpUNhCYRiGaVBYwBmGYRoUFnCGYZgGhQWcYRimQWEBZxiGaVD+H69d2N25XEZTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_in_one_day['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:35:00</th>\n",
       "      <td>90.128</td>\n",
       "      <td>306.916</td>\n",
       "      <td>166.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:40:00</th>\n",
       "      <td>111.140</td>\n",
       "      <td>222.460</td>\n",
       "      <td>149.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:45:00</th>\n",
       "      <td>103.630</td>\n",
       "      <td>247.188</td>\n",
       "      <td>153.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:50:00</th>\n",
       "      <td>126.470</td>\n",
       "      <td>219.920</td>\n",
       "      <td>169.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11 23:55:00</th>\n",
       "      <td>88.538</td>\n",
       "      <td>210.064</td>\n",
       "      <td>138.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     res_time_min  res_time_max  res_time_avg\n",
       "created_at                                                   \n",
       "2018-11-11 23:35:00        90.128       306.916         166.4\n",
       "2018-11-11 23:40:00       111.140       222.460         149.2\n",
       "2018-11-11 23:45:00       103.630       247.188         153.8\n",
       "2018-11-11 23:50:00       126.470       219.920         169.6\n",
       "2018-11-11 23:55:00        88.538       210.064         138.6"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_in_one_day =  df_in_one_day.resample('5T').mean()[['res_time_min','res_time_max','res_time_avg']]\n",
    "response_in_one_day.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f949af91b50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_in_one_day[['res_time_min', 'res_time_max', 'res_time_avg']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-11 00:00:00', '2018-11-11 00:05:00',\n",
       "               '2018-11-11 00:10:00', '2018-11-11 00:15:00',\n",
       "               '2018-11-11 00:20:00', '2018-11-11 00:25:00',\n",
       "               '2018-11-11 00:30:00', '2018-11-11 00:35:00',\n",
       "               '2018-11-11 00:40:00', '2018-11-11 00:45:00',\n",
       "               ...\n",
       "               '2018-11-11 23:10:00', '2018-11-11 23:15:00',\n",
       "               '2018-11-11 23:20:00', '2018-11-11 23:25:00',\n",
       "               '2018-11-11 23:30:00', '2018-11-11 23:35:00',\n",
       "               '2018-11-11 23:40:00', '2018-11-11 23:45:00',\n",
       "               '2018-11-11 23:50:00', '2018-11-11 23:55:00'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=288, freq='5T')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_in_one_day.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-07 19:00:00</th>\n",
       "      <td>7.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-07 20:00:00</th>\n",
       "      <td>9.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-07 21:00:00</th>\n",
       "      <td>11.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-07 22:00:00</th>\n",
       "      <td>8.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-07 23:00:00</th>\n",
       "      <td>6.366667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count\n",
       "created_at                    \n",
       "2018-11-07 19:00:00   7.900000\n",
       "2018-11-07 20:00:00   9.450000\n",
       "2018-11-07 21:00:00  11.033333\n",
       "2018-11-07 22:00:00   8.650000\n",
       "2018-11-07 23:00:00   6.366667"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_in_days =  df['2018-11-01 00:00:00':'2018-11-08 00:00:00'].resample('1H').mean()[['count']]\n",
    "response_in_days.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f949b6425b0>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(response_in_days.index, response_in_days['count'])\n",
    "# response_in_days.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "            ...\n",
       "            2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
       "           dtype='int64', name='created_at', length=168)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_in_days.index.weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.000000\n",
       "mean        187.812208\n",
       "std         224.464813\n",
       "min          36.000000\n",
       "25%         144.000000\n",
       "50%         167.000000\n",
       "75%         202.000000\n",
       "max       71325.000000\n",
       "Name: res_time_avg, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.res_time_avg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f949ae76f10>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f949ae05d30>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count'].hist(bins =30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f949ad382e0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['count']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-05-02 00:00:00</th>\n",
       "      <td>3.0</td>\n",
       "      <td>534.565</td>\n",
       "      <td>117.705</td>\n",
       "      <td>258.360</td>\n",
       "      <td>196.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 01:00:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>250.060</td>\n",
       "      <td>131.275</td>\n",
       "      <td>173.395</td>\n",
       "      <td>160.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 02:00:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>117.105</td>\n",
       "      <td>117.105</td>\n",
       "      <td>117.105</td>\n",
       "      <td>116.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 03:00:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>185.010</td>\n",
       "      <td>185.010</td>\n",
       "      <td>185.010</td>\n",
       "      <td>184.5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-02 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                             \n",
       "2019-05-02 00:00:00    3.0       534.565       117.705       258.360   \n",
       "2019-05-02 01:00:00    1.0       250.060       131.275       173.395   \n",
       "2019-05-02 02:00:00    1.0       117.105       117.105       117.105   \n",
       "2019-05-02 03:00:00    1.0       185.010       185.010       185.010   \n",
       "2019-05-02 04:00:00    NaN           NaN           NaN           NaN   \n",
       "\n",
       "                     res_time_avg  weekend  \n",
       "created_at                                  \n",
       "2019-05-02 00:00:00         196.0    False  \n",
       "2019-05-02 01:00:00         160.5    False  \n",
       "2019-05-02 02:00:00         116.5    False  \n",
       "2019-05-02 03:00:00         184.5    False  \n",
       "2019-05-02 04:00:00           NaN    False  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_may = df['2019-5-2':'2019-5-8'].resample('H').median()\n",
    "df_may['weekend'] = df_may.index.weekday.isin({5,6})\n",
    "df_may.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    6.00\n",
       "True     6.75\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_may.groupby('weekend')['count'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    5.786458\n",
       "True     5.987500\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_may.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  created_at\n",
       "False    0              2.40\n",
       "         1              1.20\n",
       "         2              1.00\n",
       "         3              1.00\n",
       "         4               NaN\n",
       "         5              1.00\n",
       "         6               NaN\n",
       "         7               NaN\n",
       "         8              1.00\n",
       "         9              1.50\n",
       "         10             1.00\n",
       "         11             2.00\n",
       "         12             4.90\n",
       "         13             7.20\n",
       "         14             9.00\n",
       "         15             9.70\n",
       "         16             8.40\n",
       "         17             7.00\n",
       "         18             6.80\n",
       "         19             9.00\n",
       "         20            11.60\n",
       "         21            11.90\n",
       "         22             9.40\n",
       "         23             6.00\n",
       "True     0              3.50\n",
       "         1              1.00\n",
       "         2              1.00\n",
       "         3              1.00\n",
       "         4              1.00\n",
       "         5               NaN\n",
       "         6               NaN\n",
       "         7               NaN\n",
       "         8              1.00\n",
       "         9              1.00\n",
       "         10             1.00\n",
       "         11             1.75\n",
       "         12             4.00\n",
       "         13             7.00\n",
       "         14             9.00\n",
       "         15            10.00\n",
       "         16            10.00\n",
       "         17             7.25\n",
       "         18             7.75\n",
       "         19             9.50\n",
       "         20            12.25\n",
       "         21            13.00\n",
       "         22            11.50\n",
       "         23             7.25\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_may.groupby(['weekend',df_may.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f949ad11d90>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_may.groupby(['weekend',df_may.index.hour])['count'].mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f949ac6f460>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_may.groupby(['weekend',df_may.index.hour])['count'].mean().unstack(level = 0).plot()"
   ]
  }
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